Digital Commons Network^™

Optimized Hybrid Watermarking: Dual-Scheme Strategies For Enhanced Robustness, Ooi Jessie, Liew Siau Chuin, Syifak Izhar Bt Hisham, Khor Hui Liang, Khoo Bee Ee, Jasni Mohamad Zain

Iraqi Journal for Computer Science and Mathematics

Digital watermarking is crucial in content identification and copyright protection, particularly multimedia and medical imaging. This paper introduces two novel hybrid watermarking methods, Entropy-Guided Singular Embedding (EGSE) and Entropy-Guided Hybrid Embedding (EGHE), that improve upon existing techniques by integrating entropy-based adaptive block selection with Particle Swarm Optimization (PSO) for dynamic embedding strength determination. Unlike traditional methods, which rely on fixed embedding regions or manual parameter tuning, the proposed approaches automatically identify high-entropy regions to embed watermark signals, ensuring stronger resistance to distortion while maintaining image quality. EGSE employs Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD), whereas EGHE enhances …

Retracted: Efficient Multi-User Computation Offloading And Reducing Latency In Mobile-Edge Computing For Iot Applications, Sarmad T. Abdul-Samad, Osamah Al-Hwaidi, Ali Abd Al-Rasool Muslem

Iraqi Journal for Computer Science and Mathematics

Mobile Edge Computing (MEC) is an inventive paradigm for computing that has the potential to notably diminish latency and energy consumption by transferring computationally demanding jobs to edge clouds near intelligent mobile users. This investigation aims to reduce offloading and latency between multiple users and edge computing in the context of Internet of Things (IoT) applications in the fifth generation (5G) by utilizing an optimization algorithm called the Bald Eagle Search Optimization Algorithm. Although employing deep learning methods might increase time consumption and computational complexity, an edge computing system enables devices to transfer their demanding jobs to edge servers, decreasing …

Retracted: Iot Flow Parameters Classification Based On Machine Learning Techniques, El-Sayed M. El-Kenawy, Marwa M. Eid, Ban Salman Shukur, Amel Ali Alhussan, Doaa Sami Khafaga

Iraqi Journal for Computer Science and Mathematics

In recent years, there has been a highly remarkable convergence of artificial intelligence (AI) and the Internet of Things (IoT), which has made rapid progress in smart city initiatives by developing smart devices for such cities. Since these devices are increasingly diversified, they require a resilient communication network to demonstrate high performance in managing consistent traffic flows. A machine learning model intended for identifying network parameters from diverse devices, in addition to proposing modifications meant for network performance enhancement, is developed in this study. In relation to packet data as a network traffic parameter, employing gateway devices can facilitate its …

Retracted: Metaguard: A Federated Learning Approach To Hybrid Xgboost And Meta-Learning Models For Proactive Cyber Threat Hunting, Shatha H. Jafer Al-Khalisy, Ghada Al-Kateb

Iraqi Journal for Computer Science and Mathematics

In an increasingly interconnected world, cybersecurity threats have become more sophisticated, necessitating advanced, scalable, and privacy-preserving solutions. MetaGuard emerges as a novel framework that integrates federated learning with hybrid machine learning models, specifically XGBoost and meta-learning, to enhance proactive cyber threat detection. This framework offers a robust, distributed approach to cybersecurity, ensuring high detection accuracy while preserving user privacy through the implementation of differential privacy and homomorphic encryption. MetaGuard leverages distributed nodes to collaboratively train a global model, enabling rapid adaptation to new threats without the need for centralized data aggregation. Experimental evaluations using the CYBER-2024 dataset demonstrate that MetaGuard …

Study On Semi-Physical Simulation Method Of Air Turbo Rocket Engine In Startup Process, Xuesen Yang, Wei Zhao, Binglong Zhang, Sanqun Ren, Xiaorong Xiang, Qingjun Zhao

Journal of System Simulation

Abstract: To satisfy the requirements for validating the control law of air turbo rocket (ATR) engines, a semi-physical simulation approach was proposed based on serial communication. This platform integrated a rapid prototype system, a supply system, a measurement and control system, a signal simulator, a fault injection system, and a real-time computer. A digital model of the engine was developed based on cross-compilation technology, enabling the coupling and semi-physical simulation of the engine control system and the supply system. A semi-physical simulation of the ATR engine in the startup process was carried out, and the fault handling strategy of the …

Research On Joint Simulation Of Special Vehicle Engine Operation Characteristics Based On Virtual Driving Scenarios, Xueyuan Xie, Chen Lin, Han Wu, Qinglan Zhao, Junfei Gao, Qiangguo Hao, Xinqian Zheng

Journal of System Simulation

Abstract: The preliminary design of the overall operation performance of diesel engines cannot be guided by actual vehicle driving tests, which hinders the improvement of the power development level and efficiency of special vehicles. By using the virtual visual simulation engine Unity3D, two virtual driving scenario models were established: a flat road scenario and an undulating road scenario. Based on the speed characteristic parameters of the engine, a diesel engine's operation performance output model was constructed. Combined with the transmission system model and the longitudinal dynamics model of the vehicle's center of mass, a straight vehicle driving dynamics model was …

Short-Term Load Forecasting Based On Dual-Attention Temporal Convolutional Long Short-Term Memory Network, Lifen Li, Jinyue Zhang, Wangbin Cao, Huawei Mei

Journal of System Simulation

Abstract: In order to improve the accuracy of load forecasting and fully extract the hidden relationships between load and other characteristic factors, a load forecasting method based on dual-attention temporal convolutional LSTM network (DA-TCLSNet) was proposed. Correlation analysis was conducted on the dataset using the maximum information coefficient method to perform feature screening to reduce the computational cost of the model. The model input was constructed using a sliding window. The DATCLSNet forecasting model was constructed. The temporal convolutional layer extracted dependencies at different time scales and captured the nonlinear characteristics among variables such as load and weather. The multi-head …

Resource Allocation Method For Virus Spreading Control Based On Multi-Granularity Cooperative Coevolution, Xuanli Shi, Weineng Chen, An Song, Tianfang Zhao

Journal of System Simulation

Abstract: According to the principle of simplifying a complex problem into sub-problems for solution, a resource allocation method for virus spreading control based on multi-granularity cooperative coevolution (MGCC) was proposed. According to the characteristics of human's social network structures, MGCC decomposed the network into sub-networks with different scales according to different decomposition granularities. A contribution-based decomposition granularity selection strategy was proposed. Historical archives were used to record the contribution of different decomposition granularities to optimization, and the appropriate decomposition granularity was selected according to the optimization status. A projection-based constraint repairing strategy was designed to ensure the feasibility of solutions. …

Lightweight Driver Face Object Detection Algorithm Based On Yolov8-Df, Mingyu Li, Jiaquan Lin

Journal of System Simulation

Abstract: The YOLOv8n detection algorithm has a large amount of computation and parameters in the driving environment. To address this issue, a lightweight driver facial object detection algorithm YOLOv8-DF was proposed. A lightweight multi-scale convolution module (LMCM) was proposed to replace the Conv module in the network, and the dual-channel design could reduce the computation and parameter quantity of the algorithm; the multi-scale design could enrich the feature information inside the network. The lightweight convolutional GhostConv, Fasterblock module, and C2f module were fused, and a dual-channel lightweight convolution module (DLCM) was fused with the SPPF module. The experimental results show …

Modeling Nato Joint Warfighting Through Space Combat Simulations, Grant Nehls, Apoorva Somani

Space and Defense

This article investigates NATO’s dependence on space systems in joint warfighting and explores the use of simulation to quantify degradation effects. Using a custom-built AFSIM model of a NATO Arctic operation, the study demonstrates how GNSS spoofing and crosslink jamming undermine data integrity, commander trust, and strike execution—even without kinetic losses. Results show that partial degradations can cause mission failure through decision paralysis. By moving beyond binary modeling, this research highlights the need for nuanced simulations to prepare NATO for contested space environments and the challenges of great power competition.

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From The Last Frontier To The Final Frontier: The Polar Regions And Space Security, Michael Lynch

Space and Defense

This article examines the intersection of Arctic geopolitics and space security amid intensifying U.S., Russian, and Chinese competition. Melting ice has opened access to resources and new sea routes, elevating the Arctic’s strategic value while increasing reliance on space-based communications, navigation, and surveillance. Yet, vulnerabilities in polar orbits, aging ground infrastructure, and dual-use facilities expose critical systems to environmental hazards and geopolitical risks. The analysis highlights modernization efforts—including SHIELD and Golden Dome—as well as enduring cooperation and rivalries, underscoring the Arctic’s role as both a frontier of opportunity and a potential flashpoint in space-enabled security.

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Same But Different: The Survivability Of Arctic-Based Russian Second-Strike Capability, July Decarpentrie

Space and Defense

This article analyzes the survivability of Russia’s second-strike capability in the Arctic through the lens of classical nuclear deterrence theory. Despite heavy commitments in Ukraine, Moscow continues to modernize its Northern Fleet and reinforce bastion defenses, underscoring the Arctic’s role as the cornerstone of its retaliatory deterrent. While these strategies mirror Cold War practices, they face unprecedented challenges from NATO expansion, multipolar competition, and advances in anti-submarine detection technologies. The study concludes that safeguarding Russia’s second-strike capability not only shapes regional security dynamics but also has broader implications for global strategic stability.

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Atomic Offsets: Nuclear Policy Shifts For Arctic States In The Wake Of Moscow’S Special Military Operation, Molly Wise, Lillian Hussong

Space and Defense

This article examines how Russia’s 2022 invasion of Ukraine has intensified nuclear reliance among Arctic states. Applying a new framework of “conventional-nuclear vulnerability,” it analyzes the nuclear policy shifts of Sweden, Finland, and Russia. The findings show that Finland and Sweden pursued NATO membership to secure nuclear guarantees, while Russia adopted riskier nuclear signaling and lowered thresholds for nuclear use. These developments highlight the growing centrality of nuclear deterrence in Arctic security, underscoring the challenges for U.S. and NATO policymakers in balancing assurance and deterrence in a volatile regional environment.

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Russian Strategic Culture And Deterrence In The Arctic, Lana Obradovic, Bethany Vailliant

Space and Defense

This study analyzes Russia’s evolving strategic culture to assess its implications for deterrence in the Arctic. It argues that Western deterrence frameworks insufficiently account for Russia’s integration of military, economic, informational, and political tools into a holistic strategy. Rooted in a siege mentality and aspirations for great power status, Russia perceives the Arctic as central to its security, economic modernization, and geopolitical influence. By situating Arctic policy within this cultural framework, the analysis highlights the need for U.S. and NATO practitioners to reassess assumptions and develop more culturally attuned deterrence strategies.

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Space And Defense Vol. 16 No. 1 Summer 2025 Special Issue On Arctic Security (Whole Issue), Editors Space And Defense

Space and Defense

Articles that cover issues on Artic Security.

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U.S News & World Report 2025 Graduate Program Rankings, Unlv & Unr, Dre Boyd-Weatherly, Caitlin J. Saladino, William E. Brown Jr.

Higher Education

This fact sheet presents 2025 graduate program rankings for the University of Nevada, Las Vegas (UNLV) and for the University of Nevada, Reno (UNR) based on the data published in spring 2025 by U.S. News & World Report. The graduate program categories reported include business, education, engineering, fine arts, law, medicine and health care, sciences and mathematics, and social sciences and humanities.

Planning And Policy For Safer Roads With Autonomous Vehicles: Moral Decision Making Behavior In Dilemma-Inducing Situations, Fatemeh Nazari, Mohamadhossein Noruzoliaee

Civil Engineering Faculty Publications

This study develops a Dynamic Bayesian Network (DBN) framework to examine how public confidence in autonomous vehicle (AV) safety and willingness-to-ride respond to policy interventions in crash-imminent pedestrian–passenger prioritization scenarios. Using stated preferences survey data from San Francisco (SF) and San Antonio (SA), the model integrates baseline attitudes, socio-demographic factors, and policy conditions to simulate both intra-slice and inter-slice dependencies. Results from empirical-mix simulations indicate that SF respondents, despite having higher baseline confidence and willingness-to-ride, exhibit greater sensitivity to policies prioritizing pedestrians, with significant declines across all scenarios. By contrast, SA respondents show comparatively stable and modestly positive shifts, particularly …

Re-Engaging Driver Attention Using Chatgpt: A Multimodal Study On Stress Impact And Driving Performance, Alaa Zaki Elfiqi

Theses and Dissertations

Driving is considered a complex task that requires continuous focus and attention from the driver. Driving performance can also be impacted by changes in the driver’s stress level. However, limited research explores methods to address this challenge. With the current advances in Large Language Models (LLMs) and their ability to engage in human-like interaction, this study investigates the potential of using ChatGPT, designed to speak Egyptian Arabic, in re-engaging driver attention under different driving scenarios for low-stress and high-stress conditions within a virtual reality (VR) environment driving simulator. Drivers were asked to follow a leading car into two scenarios with …

Depaul Digest

DePaul Magazine

College of Communication faculty Matthew Ragas and Ron Culp mentor students on gaining access to executive-level administration. News briefs on exciting developments at DePaul University’s 10 colleges and schools, from nursing students studying public health protocols in Prague to a new DePaul-hosted conference exploring AI in filmmaking. DePaul alumni volunteers share their experiences spreading the Vincentian mission nationwide.

Palpation Characteristics Of An Instrumented Virtual Cricothyroidotomy Simulator, Melih Turkseven, Trudi Di Qi, Ganesh Sankaranarayanan, Suvranu De

Engineering Faculty Articles and Research

Cricothyroidotomy (CCT) is a critical, life-saving procedure requiring the identification of key neck landmarks through palpation. Interactive virtual simulation offers a promising, cost-effective approach to CCT training with high visual realism. However, developing the palpation skills necessary for CCT requires a haptic interface with tactile sensitivity comparable to human fingers. Such interfaces are often represented by plastic partial mannequins, which require further adaptation to integrate into virtual environments. This study introduces an instrumented physical palpation interface for CCT, integrated into a virtual surgical simulator, and tested on 10 surgeons who practiced the procedure over a training period. Data on haptic …

Advancing Autonomous Swarm Behavior In A Simulated Anti-Access Area Denial Environment, Chad Macwilkinson, Lance E. Champagne, Phillip M. Lacasse

Faculty Publications

This study proposes the application of a four dimensional framework of autonomy to a weapon swarm of high subsonic cruise missiles.

Multi-Layer Support For Component-Based Cyber-Physical Systems Applications, Oren Bell

McKelvey School of Engineering Graduate Student Theses & Dissertations

Component-based design is a paradigm meant to aid in development of software applications by modularizing different functionalities of a system. This building-block approach is used extensively in cyber–physical and robotic systems. Common and established solutions to specific problems, such as perception nodes, state estimators, and motion planners, can be developed, verified, and reused as off-the-shelf modules. These modules may be integrated atop today’s heterogeneous hardware platforms, where an application can be distributed across GPUs, FPGAs, and CPU cores. When heterogeneous computational devices share the workload of a collection of components, the very act of integration may inject timing uncertainty. For …

Moment Ensemble Approaches For Estimation And Robust Quantum Control, Andre Luiz Paes De Lima

McKelvey School of Engineering Graduate Student Theses & Dissertations

Large-scale population dynamics governed by ensemble systems present significant challenges due to their inherently high dimensionality. Recent advances have demonstrated the effectiveness of moment-based methods (particularly those employing polynomial bases such as Legendre and Chebyshev polynomials) when integrated with optimization techniques for control design in ensemble systems. However, these methods have not been extensively explored for enhancing robustness against systematic noise, nor have they been widely applied to complex quantum systems involving multi-parameter configurations and entanglement phenomena. In this work, we extend the application of moment methods in ensemble systems through two projects. Initially by developing a Kalman filter analogue, …

Design And Evaluation Of An Electromagnetic Band Gap Structure For Self-Interference Reduction In Mmwave Full-Duplex Systems, Adewale Kehinde Oladeinde

Dissertations and Theses

Full-duplex (FD) wireless is a new technology that allows a device to transmit and receive at the same time and on the same frequency band. It has the potential to double the capacity and spectral efficiency of a wireless link compared to the family of conventional half-duplex wireless systems. The key challenge in implementing FD wireless communication is self-interference (SI): a node's transmitting signal generates significant interference to its receiver. Several previous studies have demonstrated the potential to reduce SI and develop FD radios; however, these studies are mostly limited to sub-6 GHz systems. Previous and on-going research explored various …

Encountering And Mitigating Selfish Mining In Bitcoin Mining Pools, Jeyasheela Rakkini M J

Theses and Dissertations

Blockchain, an innovative decentralized distributed, disrupting programming paradigm embodies key principles such as decentralization, data provenance, immutability, and transparency. At its core blockchain begins with the genesis block and progresses with each subsequent block containing the hash of the previous block, Merkle root, timestamp, a coin base transaction address, and a nonce. Miners compete to discover a target hash value (hash value of the previous block and nonce) for the current block, that is less than or equal to the difficulty value set by the system, a process known as mining.

This work encounters selfish mining attacks in bitcoin mining …

Ai Based Early Detection Of Hormonal Imbalance And Poly-Cystic Ovary Syndrome In Young Women, Reka S

Theses and Dissertations

A hormonal disorder, Poly-Cystic Ovary Syndrome (PCOS) usually affects women during the reproductive age. It is characterised by imbalances in hormones, particularly a rise in the female body's androgen level (male hormone) and enlarged ovaries with small cysts. PCOS can cause ovarian cysts, weight gain, acne, excessive hair growth, insulin resistance, and irregular menstrual cycles along with other health problems. While the exact origin of PCOS is uncertain and its symptoms are unclear, diagnosing PCOS in real-world conditions is a difficult task. Therefore, prompt and precise PCOS diagnosis is essential for efficient treatment and for averting long-term issues.

Clinicians typically …

Bioprospecting Of Wheat Straw Pyrolysis Aqueous Phase To Combat Multi-Drug Resistant Pathogens, Srividhya K

Theses and Dissertations

Hospital-acquired infections (HAIs) significantly contribute to the emergence and spread of antimicrobial resistance (AMR), primarily through the contamination of high-touch surfaces. Current disinfectants have drawbacks like high environmental persistence, ecotoxicity, and resistance development, prompting the need for environmentally friendly alternatives.

One potential approach is pyrolysis, during which the biomass components are broken down into solid, liquid, and pyro-gas. The aqueous phase of the liquid fraction is usually discarded as waste, but it contains a variety of organic compounds that have the potential for antibacterial and antifungal activity. The present study assessed the anti-infective and anti-biofilm properties of wheat straw pyrolysis …

Investigation Of Dependency Parsing Techniques For Digital Document Analysis Through Deep Learning Approach, Rekah D Ms

Theses and Dissertations

Digital document dependency parsing is a significant task in natural language processing. Dependency parsing supports verification of the grammatical correctness of a sentence besides enabling extraction of relevant documents. Inappropriate extraction of features may result in falsely parsing a document, leading to decreased accuracy. Machine learning methods have been employed to perform feature extraction. However, selecting pertinent features was never achieved which minimizes the time consumption and overhead.

Hence, novel machine learning and deep learning techniques have been designed in our work for accurate and computationally efficient digital document analytics through dependency parsing. Four different contributions have been proposed for …

Cyclone Intensity Prediction In The Bay Of Bengal Using Deep Learning Methods, Senthil Kumar J

Theses and Dissertations

The Bay of Bengal region's coastlines have been badly devastated by tropical cyclones, as the region experiences an average of five to six cyclones per year, with about two to three of these intensifying into tropical storms or severe cyclones. Thus it necessitates to study the accurate and efficient forecasting of their intensity to improve preparedness and response to natural disasters. The present study compares and examines three distinct approaches to cyclone intensity prediction using historical datasets from 1998 to 2020: hybrid optimisation, deep learning-based, and empirical approaches.The predicted accuracy, computational effectiveness, and feasibility for real-time scenarios of each model …

Design Of An Integrated Lightweight Cryptographic Algorithm For Device Level Security And Fraternal Cryptographic Algorithm For Communication Security In Medical Cyber Physical Systems, Vimala Devi P

Theses and Dissertations

Healthcare involves detecting symptoms, diagnosing conditions and giving treatment to patients. It is one of the fundamental human rights, and it might be difficult to provide healthcare to those with chronic illnesses, elderly people with disabilities and those under distant observation. The World Health Organisation (WHO) states that Cardio Vascular Disease (CVD) is the leading cause of death worldwide.

According to the prediction, CVD-related causes such as heart attacks and strokes, could result in 23.3 million deaths by 2030. In addition, the number of people with diabetes will reach 246 million; thereby, the prevalence of CVD patients and diabetics will …